Putting an eye on cytological specimens: An audit of the clinical impact of thyroid fine‐needle aspiration in different health care settings
Bibliographic record
Abstract
There is published evidence showing less cost-benefit approaches in the evaluation of thyroid nodules. We performed an institutional audit of the cytologic diagnosis of thyroid fine-needle aspiration (FNA) in an attempt to perceive the clinical impact of this technique on the management of thyroid nodules and to compare it in two different types of health care: Primary Care Medicine and Endocrinology. We performed a retrospective analysis to the electronic records of patients referred from General Practitioners (GP) and Endocrinologists (E) for thyroid FNA between 2010 and 2012. Request forms for cytological reports where retrieved for analysis of clinical and cytological data. The database search retrieved 1655 patients (female gender: 88.2%; GP references: 51.8%). Preprocedure clinical information was available from 157 out of 2005 nodules (7.8%). Significant differences in cytological diagnosis were seen in "Nondiagnostic" (GP: 11.6%; E: 7.5%, χ(2) = 0.002) and "Benign" categories (GP: 75%; E: 81.8%, χ(2) < 0.001). The main potential cause of "Nondiagnostic" samples was nodules smaller than one centimeter (total: 14 cases; GP: 7; E: 7). Reasons to request FNA for these nodules were provided in 6 out of 27 cases (GP: 0/16; E: 6/11, P < 0.001). The rate of insufficient samples was inversely correlated with nodule size (τ = -0.242, P = 0.001). When evaluating thyroid nodules, clinicians should take into account the limitations of FNA, the international recommendations for better cost-benefit approaches and the importance of a well-informed cytopathologist for better cytological diagnostic results.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.085 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".